View source on GitHub |
Base class to enqueue inputs.
tf.keras.utils.SequenceEnqueuer( sequence, use_multiprocessing=False )
The task of an Enqueuer is to use parallelism to speed up preprocessing. This is done with processes or threads.
enqueuer = SequenceEnqueuer(...) enqueuer.start() datas = enqueuer.get() for data in datas: # Use the inputs; training, evaluating, predicting. # ... stop sometime. enqueuer.close()
The enqueuer.get()
should be an infinite stream of datas.
get
get()
Creates a generator to extract data from the queue.
Skip the data if it is None
.
Generator yielding tuples `(inputs, targets)` or `(inputs, targets, sample_weights)`.
is_running
is_running()
start
start( workers=1, max_queue_size=10 )
Starts the handler's workers.
Arguments | |
---|---|
workers | Number of workers. |
max_queue_size | queue size (when full, workers could block on put() ) |
stop
stop( timeout=None )
Stops running threads and wait for them to exit, if necessary.
Should be called by the same thread which called start()
.
Arguments | |
---|---|
timeout | maximum time to wait on thread.join() |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/utils/SequenceEnqueuer